The University of Osaka · 치의학
Satoshi Yamaguchi 교수의 연구실은 디지털 치의학과 인공지능 기반 임상 예측 기술을 융합한 연구를 주도하고 있습니다. CAD/CAM 복합수복물의 탈락 예측을 위한 딥러닝 기반 영상 분석, 구강 내 색채 매칭을 향상시키기 위한 구조적 색채 재료 개발, 그리고 허브틱 기반 가상현실 교육 시스템을 통한 치의학 기술 습득 방법 개선 등 실용적임과 동시에 기술 융합에 초점을 맞추고 있습니다. 특히, 임플란트 주변 뼈의 응력 분포 분석과 복합수복물의 내구성 향상을 위한 머신러닝 기반 재료 설계도 핵심 연구 분야입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
A preventive measure for debonding has not been established and is highly desirable to improve the survival rate of computer-aided design/computer-aided manufacturing (CAD/CAM) composite resin (CR) crowns. The aim of this study was to assess the usefulness of deep learning with a convolution neural network (CNN) method to predict the debonding probability of CAD/CAM CR crowns from 2-dimensional images captured from 3-dimensional (3D) stereolithography models of a die scanned by a 3D oral scanner
D260RC producing structural color demonstrated a broad spectrum and reduction in brightness and chromatic value by adapting to surrounding restorative materials, suggesting its ability to enhance the chameleon (blending) effects to improve color matching. D260RC showed better color matching ability than resin composite containing uniformly sized ϕ150-nm SiO<sub>2</sub>-ZrO<sub>2</sub> supra-nano spherical filler.
Our aim was to evaluate haptic virtual reality (VR) simulation with repetitive training as a tool in teaching caries removal (CR) and periodontal pocket probing (PPP) skills. For the CR simulation, multilayered virtual models composed of tooth substance, caries, and pulp were developed. Seven students completed three training sessions each, which were scored based on the volume of the cut region, the number of instances of handpiece overload, and total cutting time. For the PPP task, we develope
This study reveals the effects of the design of specific components on peri-implant bone stress and abutment displacement after implant-supported single restoration in the anterior maxilla.
In silico displacement vectors in the implant fixture are insightful for geometric development of dental implants to reduce complex interactions leading to fatigue failure.
High flexural strength of computer-aided manufacturing resin composite blocks (CAD/CAM RCBs) are required in clinical scenarios. However, the conventional in vitro approach of modifying materials' composition by trial and error was not efficient to explore the effective components that contribute to the flexural strength. Machine learning (ML) is a powerful tool to achieve the above goals. Therefore, the aim of this study was to develop ML models to predict the flexural strength of CAD/CAM RCBs
The objective of this study was to assess the effect of silica nano-filler particle diameters in a computer-aided design/manufacturing (CAD/CAM) composite resin (CR) block on physical properties at the multi-scale in silico. CAD/CAM CR blocks were modeled, consisting of silica nano-filler particles (20, 40, 60, 80, and 100 nm) and matrix (Bis-GMA/TEGDMA), with filler volume contents of 55.161%. Calculation of Young's moduli and Poisson's ratios for the block at macro-scale were analyzed by homog
Between common camellia and golden camellia, hybrid plant was produced by embryo culture. The most proper season for embryo culture was late July just prior to embryo degeneration. The leaf colour, shape, serration, venation and root colour of hybrid plant were similar to its pollen parent, golden camellia.
